Abstract

We turn the Eisner algorithm for parsing to projective dependency trees into a cubic-time algorithm for parsing to a restricted class of directed graphs. To extend the algorithm into a data-driven parser, we combine it with an edge-factored feature model and online learning. We report and discuss results on the SemEval-2014 Task 8 data sets (Oepen et al., 2014).